In this experiment, we test three methods to extract the feature subspace from HSI data:
Tucker factorization of the skewness (order-3 standardized moment) [1]}, HOEVD applied to the skewness, and eigendecompostion of the covariance.
Here, skewness and covariance are formed from HSI pixels.
The results are evaluated by their performance in revealing anomalies, as in [2]}.
